Evaluating the effect of precommercial thinning on the resistance of balsam fir to windthrow through experimentation, modelling, and development of simple indices
Bibliographic record
Abstract
A tree-pulling experiment was carried out in stands of balsam fir (Abies balsamea (L.) Mill.) to evaluate the effects of early thinning on windthrow resistance. Forty trees from four stands were pulled over. Two stands had received a precommercial thinning 9 and 14 years previously, respectively, and the two others were unthinned controls. There were no significant inter-stand differences in the relationship between the critical turning moments required to overturn or snap the trees and their stem mass. The results were input into a model calculating critical wind speeds using the approach developed for the ForestGALES model. Simulations were run for four different stand densities. The mensurational characteristics for each run were taken from the results of a spacing trial established in balsam fir stands at Green River, New Brunswick. For stem breakage, the model predicted a gradual increase in critical wind speeds with wider spacing. The increase was smaller for tree overturning. The pattern of differences remained very similar after a simulated commercial thinning removing 30% of the basal area. Reductions in critical wind speeds were on the order of 4 m·s1 in all cases. Simple indices were developed that could estimate the relative results given by the model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".